a character string specifying whether genes having fold-change or p-values
below, above, or below AND above (both) the alpha value should be excluded from the dataset.
In case comparison = "both" is chosen, the cut.off argument must be a two dimensional vector defining the lower alpha value at the first position and the upper alpha value
at the second position.

alpha

a numeric value specifying the cut-off value above which Genes fulfilling the corresponding fold-change, log-fold-change, or p-value should be retained and returned by DiffGenes.

filter.method

a method how to alpha values in multiple stages. Options are "const", "min-set", and "n-set".

n

a numeric value for method = "n-set".

stage.names

a character vector specifying the new names of collapsed stages.

Details

All methods to perform dection of differentially expressed genes assume that your input
dataset has been normalized before passing it to DiffGenes. For RNA-Seq data
DiffGenes assumes that the libraries have been normalized to have the same size, i.e.,
to have the same expected column sum under the null hypothesis. If this isn't the case
please run equalizeLibSizes before calling DiffGenes.

Available methods for the detection of differentially expressed genes:

method = "smallp": Performs the method of small probabilities as proposed by Robinson and Smyth (2008) (see exactTestBySmallP for details).

method = "deviance": Uses the deviance goodness of fit statistics to define the rejection region, and is therefore equivalent to a conditional likelihood ratio test (see exactTestByDeviance for details).

Exclude non differentially expressed genes from the result dataset:

When specifying the alpha argument you furthermore, need to specify the filter.method to decide how non differentially expressed genes should be classified in multiple sample comparisons and which genes should be retained in the final dataset returned by DiffGenes. In other words, all genes < alpha based on the following filter.method are removed from the result dataset.

Following extraction criteria are implemented in this function:

const: all genes that have at least one sample comparison that undercuts or exceeds the alpha value cut.off will be excluded from the ExpressionSet. Hence, for a 7 stage ExpressionSet genes passing the alpha threshold in 6 stages will be retained in the ExpressionSet.

min-set: genes passing the alpha value in ceiling(n/2) stages will be retained in the ExpressionSet, where n is the number of stages in the ExpressionSet.

n-set: genes passing the alpha value in n stages will be retained in the ExpressionSet. Here, the argument n needs to be specified.

Note

In case input ExpressionSet objects store 0 values, internally all expression levels are
shifted by +1 to allow sufficient fold-change and p-value computations. Additionally, a warning
is printed to the console in case expression levels have been automatically shifted.